Technical Program

Paper Detail

Paper: PS-2A.36
Session: Poster Session 2A
Location: H Lichthof
Session Time: Sunday, September 15, 17:15 - 20:15
Presentation Time:Sunday, September 15, 17:15 - 20:15
Presentation: Poster
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: Neural Information Flow: Learning neural information processing systems from brain activity
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
Authors: K. Seeliger, L. Ambrogioni, U. Güçlü, M. A. J. van Gerven, Radboud University, Donders Institute for Brain, Cognition and Behaviour, Netherlands
Abstract: Neural information flow (NIF) is a new framework for system identification in neuroscience. NIF models represent neural information processing systems as coupled brain regions that each embody neural computations. These brain regions are coupled to observed data specific to that region via linear observation models. NIF models are trained via backpropagation, directly leveraging the neural signal as the loss. Trained NIF models are accessible for in silico analyses. Using a large-scale fMRI video stimulation dataset and a feed-forward convolutional neural network-based NIF model as an example we show that, in this manner, we can estimate models that learn meaningful neural computations and representations. Our framework is general in the sense that it can be used in conjunction with any neural recording techniques. It is also scalable, providing neuroscientists with a principled approach to make sense of high-dimensional neural datasets.